Leveraging Propagation for Data Mining: Models, Algorithms and Applications

B. Aditya Prakash and Naren Ramakrishnan

Department of Computer Science, Virginia Tech.

TUTORIAL - KDD 2016

Conference Link

Please find it here.

Abstract

Can we guess if a user is sick from her tweet? How do opinions get formed in online forums? Which people should we immunize, to prevent an epidemic as fast as possible? How to quickly zoom out of a graph? Graphs---also known as networks---are powerful tools for modeling processes and situations of interest in real-life like social-systems, cyber-security, epidemiology and biology. They are ubiquitous, from online social networks, gene-regulatory networks, to router graphs.

This tutorial will cover recent and state-of-the-art research on how propagation-like processes can help big-data mining specifically large networks and time-series, algorithms behind network problems, and their practical applications in various diverse settings. Topics include diffusion and virus propagation in networks, anomaly and outbreak detection, event prediction and connections with work in public health, the web and online media, social sciences/humanities and cyber security.

Outline

in PDF.

Foils

License: for education and research, you are welcome to use parts of this presentation, for free, with standard academic attribution. For-profit usage requires written permission by the authors.

in PPTX:

1. ALL slides (~44MB).
2. Part 1: Theory (~15MB).
3. Part 2: Algorithms (~6MB).
4. Part 3: Applications (~10MB).
5. Conclusions.

Video

Link to Tutorial (via videolectures)
3 videos